Feature Extraction and Image Processing

Transcription

1 Feature Extraction and Image Processing Second edition Mark S. Nixon Alberto S. Aguado :*авш JBK IIP AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO ELSEVIER Academic Press is an imprint of Elsevier

2 Contents Preface 1 Introduction Overview Human and computer vision The human vision system The eye The neural system Processing Computer vision systems Cameras Computer interfaces Processing an image Mathematical systems Mathematical tools Hello Mathcad, hello images! Hello Matlab! Associated literature Journals and magazines Textbooks The web Conclusions 2 Images, sampling and frequency domain processing 2.1 Overview 2.2 Image formation 2.3 The Fourier transform 2.4 The sampling criterion 2.5 The discrete Fourier transform One-dimensional transform Two-dimensional transform 2.6 Other properties of the Fourier transform Shift invariance Rotation Frequency scaling Superposition (linearity) XI V

3 2.7 Transforms other than Fourier Discrete cosine transform Discrete Hartley transform Introductory wavelets: the Gabor wavelet Other transforms Applications using frequency domain properties Further reading Basic image processing operations Overview Histograms Point operators Basic point operations Histogram normalization Histogram equalization Thresholding Group operations Template convolution Averaging operator On different template size Gaussian averaging operator Other statistical operators More on averaging Median filter Mode filter Anisotropic diffusion Force field transform Comparison of statistical operators Mathematical morphology Morphological operators Grey-level morphology Grey-level erosion and dilation Minkowski operators Further reading Low-level feature extraction (including edge detection) Overview First order edge detection operators Basic operators Analysis of the basic operators Prewitt edge detection operator Sobel edge detection operator Canny edge detection operator 129 vi Contents

4 Second order edge detection operators Motivation Basic operators: the Laplacian Marr-Hildreth operator Other edge detection operators Comparison of edge detection operators Further reading on edge detection Phase congruency Localized feature extraction Detecting image curvature (corner extraction) Definition of curvature Computing differences in edge direction Measuring curvature by changes in intensity (differentiation) Moravec and Harris detectors Further reading on curvature Modern approaches: region/patch analysis Scale invariant feature transform Saliency Other techniques and performance issues 4.9 Describing image motion Area-based approach Differential approach Further reading on optical flow 4.10 Conclusions Feature extraction by shape matching 5.1 Overview 5.2 Thresholding and subtraction 5.3 Template matching Definition Fourier transform implementation Discussion of template matching 5.4 Hough transform Overview Lines Hough transform for circles Hough transform for ellipses Parameter space decomposition Parameter space reduction for lines Parameter space reduction for circles Parameter space reduction for ellipses 5.5 Generalized Hough transform Formal definition of the GHT Polar definition

5 5.5.3 The GHT technique Invariant GHT Other extensions to the Hough transform Further reading Flexible shape extraction (snakes and other techniques) Overview Deformable templates Active contours (snakes) Basics The greedy algorithm for snakes Complete (Kass) snake implementation Other snake approaches Further snake developments Geometric active contours Shape skeletonization Distance transforms Symmetry Flexible shape models: active shape and active appearance Further reading Object description Overview Boundary descriptions Boundary and region Chain codes Fourier descriptors Basis of Fourier descriptors Fourier expansion Shift invariance Discrete computation Cumulative angular function Elliptic Fourier descriptors Invariance Region descriptors Basic region descriptors Moments Basic properties Invariant moments Zernike moments Other moments Further reading viii Contents

6 8 Introduction to texture description, segmentation and classification Overview What is texture? Texture description Performance requirements Structural approaches Statistical approaches Combination approaches Classification The fc-nearest neighbour rule Other classification approaches Segmentation Further reading 9 Appendix 1: Example worksheets 9.1 Example Mathcad worksheet for Chapter Example Matlab worksheet for Chapter 4 10 Appendix 2: Camera geometry fundamentals 10.1 Image geometry 10.2 Perspective camera 10.3 Perspective camera model Homogeneous coordinates and projective geometry Representation of a line and duality Ideal points Transformations in the projective space Perspective camera model analysis Parameters of the perspective camera model 10.4 Affine camera Affine camera model Affine camera model and the perspective projection Parameters of the affine camera model 10.5 Weak perspective model 10.6 Example of camera models 10.7 Discussion Appendix 3: Least squares analysis 11.1 The least squares criterion 11.2 Curve fitting by least squares

7 12 Appendix 4: Principal components analysis Introduction Data Covariance Covariance matrix Data transformation Inverse transformation Eigenproblem Solving the eigenproblem PCA method summary Example x Contents